Long noncoding RNA GAS5 ameliorates chronic constriction injury induced neuropathic pain in rats by modulation of the miR-452-5p/CELF2 axis
Bibliographic record
Abstract
Neuropathic pain is a type of spontaneous pain that causes damage to the central nervous system. Long noncoding RNAs (lncRNAs) participate in the progression of various nervous system diseases, including neuropathic pain. However, the biological function of GAS5 in neuropathic pain remains unclear. Our findings revealed that GAS5 was downregulated in chronic constriction injury (CCI) rats. Besides, ELISA showed that the concentration of IL-6, TNF-α, and IL-1β were reduced by overexpressed GAS5 in spinal cord homogenates of CCI rats. Moreover, mechanical allodynia and thermal hyperalgesia in CCI rats were inhibited by GAS5 overexpression, suggesting that GAS5 overexpression attenuated neuropathic pain. Subsequently, we found that GAS5 served as a sponge for miR-452-5p in CCI rats and CELF2 was the downstream target of miR-452-5p. Finally, through a rescue assay, we found that GAS5 ameliorated neuropathic pain in CCI rats by sponging miR-452-5p to regulate CELF2 expression. Our study confirmed that GAS5 ameliorated neuropathic pain in rats by modulation of the miR-452-5p/CELF2 axis, which may provide some clues for neuropathic pain treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".